Environmental Science & Technology · 2021 · 138 citations · 67 references
Natural SciencesEarth SciencesGeographyGeographic Information SystemsRemote SensingSpatial AnalysisLand Cover Change DetectionLand Cover MappingLand Use Change DetectionRemote Sensing ImagerySatellite Image InterpretationSatellite SensorsBuilding Height PatternBuilt-up Area MappingClassification Of Building TypesHigh-resolution Imagery AnalysisMaterial IntensityOpenstreetmap DataSatellite Remote Sensing DataSpatial Emissions Mapping
The dynamics of societal material stocks such as buildings and infrastructures and their spatial patterns drive surging resource use and emissions. Two main types of data are currently used to map stocks, night-time lights (NTL) from Earth-observing (EO) satellites and cadastral information. We present an alternative approach for broad-scale material stock mapping based on freely available high-resolution EO imagery and OpenStreetMap data. Maps of built-up surface area, building height, and building types were derived from optical Sentinel-2 and radar Sentinel-1 satellite data to map patterns of material stocks for Austria and Germany. Using material intensity factors, we calculated the mass of different types of buildings and infrastructures, distinguishing eight types of materials, at 10 m spatial resolution. The total mass of buildings and infrastructures in 2018 amounted to ∼5 Gt in Austria and ∼38 Gt in Germany (AT: ∼540 t/cap, DE: ∼450 t/cap). Cross-checks with independent data sources at various scales suggested that the method may yield more complete results than other data sources but could not rule out possible overestimations. The method yields thematic differentiations not possible with NTL, avoids the use of costly cadastral data, and is suitable for mapping larger areas and tracing trends over time.
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Combining satellite imagery and machine learning to predict poverty
Neal Jean, Marshall Burke, Sang Michael Xie et al. · Science · 2016 · 1.6K citations
Christopher D. Elvidge, Kimberly Baugh, Mikhail Zhizhin et al. · International Journal of Remote Sensing · 2017 · 923 citations · Full text